Adaptive nonlinear shape matching for unconstrained handwritten character recognition

Jeong Seon Park, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


In this paper, we propose an adaptive nonlinear shape matching method which can compensate for the various distortions in unconstrained handwritten characters. In the proposed method, structural information is incorporated to improve the accuracy of matching, and only neighboring pixels of each black pixel are considered to reduce the computational complexity of single matching procedure. Also, iterative nonlinear shape matching procedures in each subregion are adaptively accomplished according to the results of that subregion, in order to accelerate the convergence speed of the matching procedure. In order to verify the performance of the proposed method, experiments with large-set unconstrained handwritten Hangul character database PE92 have been performed. Experimental results reveal that the proposed method is superior to the previous nonlinear shape matching method in processing speed and accuracy of matching.

Original languageEnglish
Pages (from-to)1223-1235
Number of pages13
JournalPattern Recognition
Issue number8
Publication statusPublished - 1995 Aug
Externally publishedYes


  • Adaptive nonlinear shape matching
  • Hangul character recognition
  • Local affine transformation
  • Unconstrained handwritten character recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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